A Forest Fire Identification System Based on Weighted Fusion Algorithm

Qian, Jingjing; Lin, Haifeng

doi:10.3390/f13081301

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…This improved model enables real-time detection of multi-scale forest fires, making it suitable for timely response and intervention in fire incidents. An alternative approach to address the aforementioned problem is described in the research conducted by Qian et al [ 112 ]. Their work presents an algorithm that utilizes weighted fusion to identify forest fire sources in various scenarios.…”

Section: The Role Of Traditional Machine Learning and Deep Learning I...mentioning

confidence: 99%

Advancements in Forest Fire Prevention: A Comprehensive Survey

Carta,

Zidda,

Putzu

et al. 2023

Sensors

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Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires.

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Section: The Role Of Traditional Machine Learning and Deep Learning I...mentioning

confidence: 99%

Advancements in Forest Fire Prevention: A Comprehensive Survey

Carta,

Zidda,

Putzu

et al. 2023

Sensors

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show abstract

“…The authors in [16] have used a Machine Learning approach called Random Forest (RF) that maps the regional distribution of fire risk and determines how climatic and human-caused factors affect the likelihood of a fire occurring. Yolov5 and EfficientDet are used by the authors in [17] to extract the forest fire features and prove to be more efficient than regular manual feature extraction.…”

Section: Disastermentioning

confidence: 99%

Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires

Gadekallu

2023

Sustainability

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Forests are a vital part of the ecological system. Forest fires are a serious issue that may cause significant loss of life and infrastructure. Forest fires may occur due to human or man-made climate effects. Numerous artificial intelligence-based strategies such as machine learning (ML) and deep learning (DL) have helped researchers to predict forest fires. However, ML and DL strategies pose some challenges such as large multidimensional data, communication lags, transmission latency, lack of processing power, and privacy concerns. Federated Learning (FL) is a recent development in ML that enables the collection and process of multidimensional, large volumes of data efficiently, which has the potential to solve the aforementioned challenges. FL can also help in identifying the trends based on the geographical locations that can help the authorities to respond faster to forest fires. However, FL algorithms send and receive large amounts of weights of the client-side trained models, and also it induces significant communication overhead. To overcome this issue, in this paper, we propose a unified framework based on FL with a particle swarm-optimization algorithm (PSO) that enables the authorities to respond faster to forest fires. The proposed PSO-enabled FL framework is evaluated by using multidimensional forest fire image data from Kaggle. In comparison to the state-of-the-art federated average model, the proposed model performed better in situations of data imbalance, incurred lower communication costs, and thus proved to be more network efficient. The results of the proposed framework have been validated and 94.47% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of early warning systems for forest fires.

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“…However, this method ignores the problem of incomplete detection of occluded targets. Qian and Lin [26] fused two independent weakly supervised models, YOLOv5 and EfficientDet, to achieve algorithmic improvement, improving the accuracy and detection speed. But the required huge computation costs lead to the lack of practicality.…”

Section: Introductionmentioning

confidence: 99%

Fire Detection and Flame-Centre Localisation Algorithm Based on Combination of Attention-Enhanced Ghost Mode and Mixed Convolution

Liu,

Yin,

Yang

2024

Applied Sciences

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This paper proposes a YOLO fire detection algorithm based on an attention-enhanced ghost mode, mixed convolutional pyramids, and flame-centre detection (AEGG-FD). Specifically, the enhanced ghost bottleneck is stacked to reduce redundant feature mapping operations in the process for achieving lightweight reconfiguration of the backbone, while attention is added to compensate for accuracy loss. Furthermore, a feature pyramid built using mixed convolution is introduced to accelerate network inference speed. Finally, the local information is extracted by the designed flame-centre detection (FD) module for furnishing auxiliary information in effective firefighting. Experimental results on both the benchmark fire dataset and the video dataset show that the AEGG-FD performs better than the classical YOLO-based models such as YOLOv5, YOLOv7 and YOLOv8. Specifically, both the mean accuracy (mAP0.5, reaching 84.7%) and the inferred speed (FPS) are improved by 6.5 and 8.4 respectively, and both the number of model parameters and model size are compressed to 72.4% and 44.6% those of YOLOv5, respectively. Therefore, AEGG-FD achieves an effective balance between model weight, detection speed, and accuracy in firefighting.

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A Forest Fire Identification System Based on Weighted Fusion Algorithm

Cited by 15 publications

References 21 publications

Advancements in Forest Fire Prevention: A Comprehensive Survey

Advancements in Forest Fire Prevention: A Comprehensive Survey

Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires

Fire Detection and Flame-Centre Localisation Algorithm Based on Combination of Attention-Enhanced Ghost Mode and Mixed Convolution

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